Sensory prediction errors, not performance errors, update memories in visuomotor adaptation

Lee, Kangwoo; Oh, Youngmin; Izawa, Jun; Schweighofer, Nicolas

doi:10.1038/s41598-018-34598-y

Cited by 23 publications

(15 citation statements)

References 26 publications

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“…Despite these differences, the recent studies showing that opposing force fields can be learned when different plans are associated with identical trajectories (Hirashima and Nozaki 2012; Sheahan et al 2016, 2018) indicate that principles similar to those we found in this study may apply across kinematic and dynamic transformations. It should be noted that our present results extend these findings by showing that the plan does not need to be tied to a visual target, in line with learning from sensory prediction errors being independent of visual target presence (Lee et al 2018). The extent to which plan-dependent force compensation can be characterized as explicit or implicit remains to be clarified.…”

Section: Discussionsupporting

confidence: 87%

Plan-based generalization shapes local implicit adaptation to opposing visuomotor transformations

Schween

Taylor

Hegele

2018

Journal of Neurophysiology

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The human ability to use different tools demonstrates our capability of forming and maintaining multiple, context specific motor memories. Experimentally, this has been investigated in dual adaptation, where participants adjust their reaching movements to opposing visuomotor transformations. Adaptation in these paradigms occurs by distinct processes, such as strategies for each transformation or the implicit acquisition of distinct visuomotor mappings. While distinct, transformation-dependent aftereffects have been interpreted as support for the latter, they could reflect adaptation of a single visuomotor map, which is locally adjusted in different regions of the workspace. Indeed, recent studies suggest that explicit aiming strategies direct where in the workspace implicit adaptation occurs, thus potentially serving as a cue to enable dual adaptation. Disentangling these possibilities is critical to understanding how humans acquire and maintain motor memories for different skills and tools. We therefore investigated generalization of explicit and implicit adaptation to untrained movement directions after participants practiced two opposing cursor rotations, which were associated with the visual display being presented in the left or right half of the screen. Whereas participants learned to compensate for opposing rotations by explicit strategies specific to this visual workspace cue, aftereffects were not cue-sensitive. Instead, aftereffects displayed bimodal generalization patterns that appeared to reflect locally limited learning of both transformations. By varying target arrangements and instructions, we show that these patterns are consistent with implicit adaptation that generalizes locally around movement plans associated with opposing visuomotor transformations. Our findings show that strategies can shape implicit adaptation in a complex manner.

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Section: Discussionsupporting

confidence: 87%

Plan-based generalization shapes local implicit adaptation to opposing visuomotor transformations

Schween

Taylor

Hegele

2018

Journal of Neurophysiology

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“…Interestingly, the large exclusion in the IR groups actually decreases over time, so that when it is measured several minutes after adaptation, as part of the process dissociation procedure, subjects in all groups have the same amount of exclusion (Figure 6b). Some have reported that implicit adaptation is composed of stable and labile components (Hadjiosif & Smith, 2013;McDougle et al, 2015;Miyamoto et al, 2014;Morehead, 2018), and it may be that intermittent reporting leads to more labile implicit, which, in part, explains the decay of the exclusion in the IR groups (Figure 6c). Since temporally labile implicit has been shown to decay at a time constant of 15-30 s (Hadjiosif & Smith, 2013;Morehead, 2018), one could postulate that our intermittent measures may have been affected by this decay.…”

Section: Discussionmentioning

confidence: 98%

“…The consistency of this result across individuals is shown in Figure 7c, left scatter. In the discussion, we consider the possibility that this difference is the result of decay in a labile component of implicit adaptation caused by the time delay between the end of adaptation and the posttest (Hadjiosif & Smith, 2013;Kim et al, 2019;Miyamoto et al, 2014;Morehead, 2018).…”

Section: Posttestmentioning

confidence: 99%

“…For instance, McDougle and Taylor (2019) propose that explicit re-aiming may have different components that are computed and cached (McDougle & Taylor, 2019). Others suggest that implicit adaptation has labile and stable components (Heuer & Hegele, 2015;Kim, Parvin, & Ivry, 2019;Miyamoto, Wang, Brennan, & Smith, 2014). One possibility is that different measures weigh the components differently.…”

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confidence: 99%

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Methods matter: Your measures of explicit and implicit processes in visuomotor adaptation affect your results

Maresch

Werner

Donchin

2020

Eur J of Neuroscience

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Visuomotor rotations are frequently used to study the different processes underlying motor adaptation. Explicit aiming strategies and implicit recalibration are two of these processes. Various methods, which differ in their underlying assumptions, have been used to dissociate the two processes. Direct methods, such as verbal reports, assume explicit knowledge to be verbalizable, where indirect methods, such as the exclusion, assume that explicit knowledge is controllable. The goal of this study was thus to directly compare verbal reporting with exclusion in two different conditions: during consistent reporting and during intermittent reporting. Our results show that our two conditions lead to a dissociation between the measures. In the consistent reporting group, all measures showed similar results. However, in the intermittent reporting group, verbal reporting showed more explicit re‐aiming and less implicit adaptation than exclusion. Curiously, when exclusion was measured again, after the end of learning, the differences were no longer apparent. We suspect this may reflect selective decay in implicit adaptation, as has been reported previously. All told, our results clearly indicate that methods of measurement can affect the amount of explicit re‐aiming and implicit adaptation that is measured. Since it has been previously shown that both explicit re‐aiming and implicit adaptation have multiple components, discrepancies between these different methods may arise because different measures reflect different components.

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“…Lee and Schweighofer, 2009;Kambara et al, 2011) and of the body (Cothros et al, 2006;Kording et al, 2007;Berniker and Kording, 2008;Kluzik et al, 2008). Internal models are updated to minimize sensory prediction errors, i.e., errors between sensory outcomes and predictions (Mazzoni and Krakauer, 2006;Taylor and Ivry, 2011;K. Lee et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Minimizing Precision-Weighted Sensory Prediction Errors via Memory Formation and Switching in Motor Adaptation

Schweighofer

2019

J. Neurosci.

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Humans predict the sensory consequences of motor commands by learning internal models of the body and of environment perturbations. When facing a sensory prediction error, should we attribute this error to a change in our body, and update the body internal model, or to a change in the environment? In the latter case, should we update an existing perturbation model or create a new model? Here, we propose that a decision-making process compares the models' prediction errors, weighted by their precisions, to select and update either the body model or an existing perturbation model. When no model can predict a perturbation, a new perturbation model is created and selected. When a model is selected, both the prediction's mean estimate and uncertainty are updated to minimize future prediction errors and to increase the precision of the predictions. Results from computer simulations, which we verified in an arm visuomotor adaptation experiment with subjects of both sexes, account for short aftereffects and large savings after adaptation to large, but not small, perturbations. Results also clarify previous data in the absence of errors (error-clamp): motor memories show an initial lack of decay after a large perturbation, but gradual decay after a small perturbation. Finally, qualitative individual differences in adaptation were explained by subjects selecting and updating either the body model or a perturbation model. Our results suggest that motor adaptation belongs to a general class of learning according to which memories are created when no existing memories can predict sensory data accurately and precisely.

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Sensory prediction errors, not performance errors, update memories in visuomotor adaptation

Cited by 23 publications

References 26 publications

Plan-based generalization shapes local implicit adaptation to opposing visuomotor transformations

Plan-based generalization shapes local implicit adaptation to opposing visuomotor transformations

Methods matter: Your measures of explicit and implicit processes in visuomotor adaptation affect your results

Minimizing Precision-Weighted Sensory Prediction Errors via Memory Formation and Switching in Motor Adaptation

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